Hi Jim, thanks for the feedback!
The data gaps will be filled when a full reprocessing is finished. I hesitate to use NaN or other placeholders. This will be used when there are real data gaps not for incomplete processing.
Earlier you pointed out the ordering. I missed a .sort_index() which is now included.
I updated the README.txt to describe the mask used:
Regional sea ice extent and area are calculated based on the upsampled 25 km "NSIDC" regional mask "Arctic_region_mask_Meier_AnnGlaciol2007.msk" available at
https://nsidc.org/data/g02135The old 25 km mask is certainly not the best choice for the 3.125 km product because of inconsistencies with the land mask.
Regarding the format I leave it to the users to divide by 1e6. Using python the data can be easily used this way:
import pandas as pd
amsr2=pd.read_csv(extent_merged,index_col='Date',parse_dates=[0])/1e6
amsr2_1day=amsr2.resample("1d").mean()
Assuming for the moment that Lars is watching:
Feedback and comments are very welcome!
There are obviously still some large gaps in the data, and less obviously some small ones. In the CSV files could you fill missing dates with NaN or -1 or somesuch?
A personal preference, but could you format area/extent values in million km2?
Is there a binary mask of Wipenus / Cryosphere today regions somewhere?
I assume Wipneus must have one somewhere! As a corollary, could you make the 2432x3584 regional mask you are presumably using available?